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| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| import torchaudio | |
| import torch | |
| import os | |
| from pydub import AudioSegment | |
| # Get the directory of the current file | |
| current_dir = os.path.dirname(os.path.abspath(__file__)) | |
| # Construct the absolute path to the 'ffmpeg/bin' directory | |
| ffmpeg_bin_path = os.path.join(current_dir, 'ffmpeg', 'bin') | |
| # Add this path to the PATH environment variable | |
| os.environ["PATH"] += os.pathsep + ffmpeg_bin_path | |
| # Ensure ffmpeg is in PATH | |
| AudioSegment.converter = os.path.join(ffmpeg_bin_path, 'ffmpeg.exe') | |
| # load model and processor | |
| processor = WhisperProcessor.from_pretrained("openai/whisper-small") | |
| model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") | |
| model.config.forced_decoder_ids = None | |
| def audio_to_text(webm_file_path): | |
| wav_file = "recorded_audio.wav" | |
| absolute_path = os.path.abspath(webm_file_path) | |
| # Load and convert audio | |
| # Check if the file exists | |
| if os.path.exists(webm_file_path): | |
| wav_audio = AudioSegment.from_file(absolute_path, format="webm") | |
| wav_audio.export(wav_file, format="wav") | |
| # Load the audio and resample it | |
| waveform, sample_rate = torchaudio.load('recorded_audio.wav') | |
| resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000) | |
| waveform = resampler(waveform) | |
| waveform = waveform.squeeze().numpy() | |
| input_features = processor(waveform, sampling_rate=16000, return_tensors="pt").input_features | |
| # generate token ids | |
| predicted_ids = model.generate(input_features) | |
| # decode token ids to text | |
| transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
| return transcription | |
| else: | |
| return None |